spaCy/website/docs/api/entitylinker.md

title	tag	source	new	teaser	api_base_class	api_string_name	api_trainable
EntityLinker	class	spacy/pipeline/entity_linker.py	2.2	Pipeline component for named entity linking and disambiguation	/api/pipe	entity_linker	true

An EntityLinker component disambiguates textual mentions (tagged as named entities) to unique identifiers, grounding the named entities into the "real world". It requires a KnowledgeBase, as well as a function to generate plausible candidates from that KnowledgeBase given a certain textual mention, and a ML model to pick the right candidate, given the local context of the mention.

Config and implementation

The default config is defined by the pipeline component factory and describes how the component should be configured. You can override its settings via the config argument on nlp.add_pipe or in your config.cfg for training. See the model architectures documentation for details on the architectures and their arguments and hyperparameters.

Example

from spacy.pipeline.entity_linker import DEFAULT_NEL_MODEL
config = {
   "labels_discard": [],
   "incl_prior": True,
   "incl_context": True,
   "model": DEFAULT_NEL_MODEL,
   "kb_loader": {'@assets': 'spacy.EmptyKB.v1', 'entity_vector_length': 64},
   "get_candidates": {'@assets': 'spacy.CandidateGenerator.v1'},
}
nlp.add_pipe("entity_linker", config=config)

Setting	Type	Description	Default
labels_discard	Iterable[str]	NER labels that will automatically get a "NIL" prediction.	[]
incl_prior	bool	Whether or not to include prior probabilities from the KB in the model.	True
incl_context	bool	Whether or not to include the local context in the model.	True
model	Model	The model to use.	EntityLinker
kb_loader	Callable[[Vocab], KnowledgeBase]	Function that creates a KnowledgeBase from a Vocab instance.	An empty KnowledgeBase with entity_vector_length 64.
get_candidates	Callable[[KnowledgeBase, "Span"], Iterable[Candidate]]	Function that generates plausible candidates for a given Span object.	Built-in dictionary-lookup function.

https://github.com/explosion/spaCy/blob/develop/spacy/pipeline/entity_linker.py

EntityLinker.__init__

Example

# Construction via add_pipe with default model
entity_linker = nlp.add_pipe("entity_linker")

# Construction via add_pipe with custom model
config = {"model": {"@architectures": "my_el.v1"}}
entity_linker = nlp.add_pipe("entity_linker", config=config)

# Construction via add_pipe with custom KB and candidate generation
config = {"kb_loader": {"@assets": "my_kb.v1"}, "get_candidates": {"@assets": "my_candidates.v1"},}
entity_linker = nlp.add_pipe("entity_linker", config=config)

# Construction from class
from spacy.pipeline import EntityLinker
entity_linker = EntityLinker(nlp.vocab, model)

Create a new pipeline instance. In your application, you would normally use a shortcut for this and instantiate the component using its string name and nlp.add_pipe.

Note that both the internal KB as well as the Candidate generator can be customized by providing custom registered functions.

Name	Type	Description
vocab	Vocab	The shared vocabulary.
model	Model	The Model powering the pipeline component.
name	str	String name of the component instance. Used to add entries to the losses during training.
keyword-only
kb_loader	Callable[[Vocab], KnowledgeBase]	Function that creates a KnowledgeBase from a Vocab instance.
get_candidates	Callable[[KnowledgeBase, "Span"], Iterable[Candidate]]	Function that generates plausible candidates for a given Span object.
labels_discard	Iterable[str]	NER labels that will automatically get a "NIL" prediction.
incl_prior	bool	Whether or not to include prior probabilities from the KB in the model.
incl_context	bool	Whether or not to include the local context in the model.

EntityLinker.__call__

Apply the pipe to one document. The document is modified in place, and returned. This usually happens under the hood when the nlp object is called on a text and all pipeline components are applied to the Doc in order. Both __call__ and pipe delegate to the predict and set_annotations methods.

Example

doc = nlp("This is a sentence.")
entity_linker = nlp.add_pipe("entity_linker")
# This usually happens under the hood
processed = entity_linker(doc)

Name	Type	Description
doc	Doc	The document to process.
RETURNS	Doc	The processed document.

EntityLinker.pipe

Apply the pipe to a stream of documents. This usually happens under the hood when the nlp object is called on a text and all pipeline components are applied to the Doc in order. Both __call__ and pipe delegate to the predict and set_annotations methods.

Example

entity_linker = nlp.add_pipe("entity_linker")
for doc in entity_linker.pipe(docs, batch_size=50):
    pass

Name	Type	Description
stream	Iterable[Doc]	A stream of documents.
keyword-only
batch_size	int	The number of texts to buffer. Defaults to 128.
YIELDS	Doc	Processed documents in the order of the original text.

EntityLinker.begin_training

Initialize the component for training and return an Optimizer. get_examples should be a function that returns an iterable of Example objects. The data examples are used to initialize the model of the component and can either be the full training data or a representative sample. Initialization includes validating the network, inferring missing shapes and setting up the label scheme based on the data.

Example

entity_linker = nlp.add_pipe("entity_linker", last=True)
optimizer = entity_linker.begin_training(lambda: [], pipeline=nlp.pipeline)

Name	Type	Description
get_examples	Callable[[], Iterable[Example]]	Optional function that returns gold-standard annotations in the form of Example objects.
keyword-only
pipeline	List[Tuple[str, Callable]]	Optional list of pipeline components that this component is part of.
sgd	Optimizer	An optional optimizer. Will be created via create_optimizer if not set.
RETURNS	Optimizer	The optimizer.

EntityLinker.predict

Apply the component's model to a batch of Doc objects, without modifying them. Returns the KB IDs for each entity in each doc, including NIL if there is no prediction.

Example

entity_linker = nlp.add_pipe("entity_linker")
kb_ids = entity_linker.predict([doc1, doc2])

Name	Type	Description
docs	Iterable[Doc]	The documents to predict.
RETURNS	List[str]	The predicted KB identifiers for the entities in the docs.

EntityLinker.set_annotations

Modify a batch of documents, using pre-computed entity IDs for a list of named entities.

Example

entity_linker = nlp.add_pipe("entity_linker")
kb_ids = entity_linker.predict([doc1, doc2])
entity_linker.set_annotations([doc1, doc2], kb_ids)

Name	Type	Description
docs	Iterable[Doc]	The documents to modify.
kb_ids	List[str]	The knowledge base identifiers for the entities in the docs, predicted by EntityLinker.predict.

EntityLinker.update

Learn from a batch of Example objects, updating both the pipe's entity linking model and context encoder. Delegates to predict.

Example

entity_linker = nlp.add_pipe("entity_linker")
optimizer = nlp.begin_training()
losses = entity_linker.update(examples, sgd=optimizer)

Name	Type	Description
examples	Iterable[Example]	A batch of Example objects to learn from.
keyword-only
drop	float	The dropout rate.
set_annotations	bool	Whether or not to update the Example objects with the predictions, delegating to set_annotations.
sgd	Optimizer	The optimizer.
losses	Dict[str, float]	Optional record of the loss during training. Updated using the component name as the key.
RETURNS	Dict[str, float]	The updated losses dictionary.

EntityLinker.create_optimizer

Create an optimizer for the pipeline component.

Example

entity_linker = nlp.add_pipe("entity_linker")
optimizer = entity_linker.create_optimizer()

Name	Type	Description
RETURNS	Optimizer	The optimizer.

EntityLinker.use_params

Modify the pipe's model, to use the given parameter values. At the end of the context, the original parameters are restored.

Example

entity_linker = nlp.add_pipe("entity_linker")
with entity_linker.use_params(optimizer.averages):
    entity_linker.to_disk("/best_model")

Name	Type	Description
params	dict	The parameter values to use in the model.

EntityLinker.to_disk

Serialize the pipe to disk.

Example

entity_linker = nlp.add_pipe("entity_linker")
entity_linker.to_disk("/path/to/entity_linker")

Name	Type	Description
path	str / Path	A path to a directory, which will be created if it doesn't exist. Paths may be either strings or Path-like objects.
keyword-only
exclude	Iterable[str]	String names of serialization fields to exclude.

EntityLinker.from_disk

Load the pipe from disk. Modifies the object in place and returns it.

Example

entity_linker = nlp.add_pipe("entity_linker")
entity_linker.from_disk("/path/to/entity_linker")

Name	Type	Description
path	str / Path	A path to a directory. Paths may be either strings or Path-like objects.
keyword-only
exclude	Iterable[str]	String names of serialization fields to exclude.
RETURNS	EntityLinker	The modified EntityLinker object.

Serialization fields

During serialization, spaCy will export several data fields used to restore different aspects of the object. If needed, you can exclude them from serialization by passing in the string names via the exclude argument.

Example

data = entity_linker.to_disk("/path", exclude=["vocab"])

Name	Description
vocab	The shared Vocab.
cfg	The config file. You usually don't want to exclude this.
model	The binary model data. You usually don't want to exclude this.
kb	The knowledge base. You usually don't want to exclude this.